Uppsala University

About: Uppsala University is a education organization based out in Uppsala, Sweden. It is known for research contribution in the topics: Population & Gene. The organization has 36485 authors who have published 107509 publications receiving 4220668 citations. The organization is also known as: Uppsala universitet & uu.se.

Association of Cardiometabolic Multimorbidity With Mortality

Abstract: IMPORTANCE: The prevalence of cardiometabolic multimorbidity is increasing. OBJECTIVE: To estimate reductions in life expectancy associated with cardiometabolic multimorbidity. DESIGN, SETTING, AND PARTICIPANTS: Age- and sex-adjusted mortality rates and hazard ratios (HRs) were calculated using individual participant data from the Emerging Risk Factors Collaboration (689,300 participants; 91 cohorts; years of baseline surveys: 1960-2007; latest mortality follow-up: April 2013; 128,843 deaths). The HRs from the Emerging Risk Factors Collaboration were compared with those from the UK Biobank (499,808 participants; years of baseline surveys: 2006-2010; latest mortality follow-up: November 2013; 7995 deaths). Cumulative survival was estimated by applying calculated age-specific HRs for mortality to contemporary US age-specific death rates. EXPOSURES: A history of 2 or more of the following: diabetes mellitus, stroke, myocardial infarction (MI). MAIN OUTCOMES AND MEASURES: All-cause mortality and estimated reductions in life expectancy. RESULTS: In participants in the Emerging Risk Factors Collaboration without a history of diabetes, stroke, or MI at baseline (reference group), the all-cause mortality rate adjusted to the age of 60 years was 6.8 per 1000 person-years. Mortality rates per 1000 person-years were 15.6 in participants with a history of diabetes, 16.1 in those with stroke, 16.8 in those with MI, 32.0 in those with both diabetes and MI, 32.5 in those with both diabetes and stroke, 32.8 in those with both stroke and MI, and 59.5 in those with diabetes, stroke, and MI. Compared with the reference group, the HRs for all-cause mortality were 1.9 (95% CI, 1.8-2.0) in participants with a history of diabetes, 2.1 (95% CI, 2.0-2.2) in those with stroke, 2.0 (95% CI, 1.9-2.2) in those with MI, 3.7 (95% CI, 3.3-4.1) in those with both diabetes and MI, 3.8 (95% CI, 3.5-4.2) in those with both diabetes and stroke, 3.5 (95% CI, 3.1-4.0) in those with both stroke and MI, and 6.9 (95% CI, 5.7-8.3) in those with diabetes, stroke, and MI. The HRs from the Emerging Risk Factors Collaboration were similar to those from the more recently recruited UK Biobank. The HRs were little changed after further adjustment for markers of established intermediate pathways (eg, levels of lipids and blood pressure) and lifestyle factors (eg, smoking, diet). At the age of 60 years, a history of any 2 of these conditions was associated with 12 years of reduced life expectancy and a history of all 3 of these conditions was associated with 15 years of reduced life expectancy. CONCLUSIONS AND RELEVANCE: Mortality associated with a history of diabetes, stroke, or MI was similar for each condition. Because any combination of these conditions was associated with multiplicative mortality risk, life expectancy was substantially lower in people with multimorbidity.

Rapid Regulation of Light Harvesting and Plant Fitness in the Field

Abstract: We used Arabidopsis thaliana mutants to examine how a photosynthetic regulatory process, the qE-type or ΔpH-dependent nonphotochemical quenching, hereafter named feedback de-excitation, influences plant fitness in different light environments. We show that the feedback de-excitation is important for plant fitness in the field and in fluctuating light in a controlled environment but that it does not affect plant performance under constant light conditions. Our findings demonstrate that the feedback de-excitation confers a strong fitness advantage under field conditions and suggest that this advantage is due to the increase in plant tolerance to variation in light intensity rather than tolerance to high-intensity light itself.

Elasticities: a review of methods and model limitations

Abstract: Elasticity is a perturbation measure in matrix projection models that quantifies the proportional change in population growth rate as a function of a proportional change in a demographic transition (growth, survival, reproduction, etc.). Elasticities thus indicate the relative “importance” of life cycle transitions for population growth and maintenance. In this paper, we discuss the applications of elasticity analysis, and its extension, loop analysis, in life history studies and conservation. Elasticity can be interpreted as the relative contribution of a demographic parameter to population growth rate. Loop analysis reveals the underlying pathway structure of the life cycle graph. The different kinds of results of the two analyses in studies of life histories are emphasized. Because elasticities quantify the relative importance of life cycle transitions to population growth rate, it is generally inferred that management should focus on the transitions with the largest elasticities. Such predictions based on elasticities seem robust, but we do identify three situations where problems may arise. The mathematical properties and biological constraints that underlie these pitfalls are explained. Examples illustrate the additional information that needs to be taken into account for a sensible use of elasticities in population management. We conclude by indicating topics that are in need of research.